Collaborative mobile services
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Mobile devices like smartphones and tablets are being adopted with unprecedented speed. The growth in demand and system complexity increasingly requires collaboration of multiple parties in order to achieve better functionality, efficiency, performance, etc. This poses unique challenges such as information sharing among different parties, utility sharing among different parties, and dishonest and collusive behaviors. Different mobile services may require different types of collaboration and involve different entities in the system. In this work we take a bottom-up approach by first looking at collaboration at the end user level, then the cross level collaboration and finally at the service provider level. Specifically, we first consider a completely distributed service: friend discovery in mobile social networks, where users of a mobile social network work together with each other to discover potential new friends nearby by computing their social proximity. We develop mathematically sound yet highly efficient approaches that simultaneously achieve privacy and verifiability. We then focus on cellular offloading where a cellular service provider seeks third party resource to offload cellular demand, as an example of cross level collaboration. We propose a reverse auction framework: iDEAL, which efficiently allocates cellular resource and third party resource in a joint optimization, effectively incentivize third party resource owners and mitigates dishonest and collusive behaviors. We validate our findings and approaches with real trace driven analysis and simulation, as well as real implementation. Finally we focus on collaboration at the service provider level and propose a double auction framework - DA². DA² allows cellular service providers to reallocate spectrum resource in a dynamic fashsion. It preserves all the desired economic properties. Compared with existing spectrum double auctions, DA² achieves higher efficiency, revenue, and spectrum resource utilization, due to its ability to more accurately capture the competition among buyers, which is characterized by a complex conflict graph. We evaluate DA² and demonstrate its superior performance via simulations on conflict graphs generated with real cell tower locations.